Steven,
A quick trick to deal with problems of this nature (i.e. forcing
parameters to stay positive although the parameter search isn't bound
to non-negatives or positives) without resorting to another package is
to re-parametrize the density function.
Notice,
x \in (-\infty,\infty) for x \in (-
-help-boun...@r-project.org] On
Behalf Of Prof. John C Nash
Sent: Monday, December 07, 2009 9:01 AM
To: r-help@r-project.org
Subject: Re: [R] optim with constraints
Without the data / script, I'm guessing that it is likely an attempt to
evaluate the loss
function at an inadmissible point e.g., at
other things) on trying to provide a nice wrapper to catch
these exceptions so that they can be handled better.
JN
Message: 56
Date: Sun, 6 Dec 2009 17:32:54 -0800 (PST)
From: Steven
Subject: Re: [R] optim with constraints
To: r-help@r-project.org
Message-ID:
<88d5c01d-c30c-4
Hi, Prof Nash
Thanks for your comment!
I modified my code to be (added an extra parametr):
optim(c(1.14,0.25,0.06), weibull.like, mydata=mydata, method="L-BFGS-
B", hessian = TRUE, lower = c(0, 0, 0), upper = c(Inf, Inf, 1))
But I had the following error:
Error in optim(c(1.14, 0.25, 0.06), we
Read the help page for `optim'. You already seem to be aware that "L-BFGS-B"
should be used. How much more harder is to read the help page further to
figure out how to supply the constraints?
There are also various other algorithms, but "L-BFGS-B" should be able to do
the job.
Ravi.
__
As per ?optim
Usage:
optim(par, fn, gr = NULL, ...,
method = c("Nelder-Mead", "BFGS", "CG", "L-BFGS-B", "SANN"),
lower = -Inf, upper = Inf,
control = list(), hessian = FALSE)
Note that the optimx() function on R-forge
http://r-forge.r-project.org/R/?grou
6 matches
Mail list logo
